We present the progression of developments necessary to scale the ISAM land surface model from single nodes and small clusters with unusually large per-node memory to much larger systems with more common configurations. These efforts include load balancing, conventional library-based output parallelization to reduce memory load, and parallel-in-time data input. On Hopper, a Cray XE6 machine, the result was strong scaling from 256 cores to 16k cores with an efficiency of 32.9%. On Edison, a Cray XC30 machine, the code strong scales from 256 cores to 16k cores with an efficiency of 51.4%. These large-scale gains, and the associated performance increases at smaller scale, enable greater scientific productivity for the users of ISAM and open the possibilities of increased resolution in time and space and greater physical fidelity for the simulated processes while remaining computationally feasible.